Paper Info
Title
Sequential Bayesian Design for Accelerated Life Tests.
Abstract
Most of the recently developed methods on optimum planning for accelerated life tests (ALT) involve "guessing" values of parameters to be estimated, and substituting such guesses in the proposed solution to obtain the final testing plan. In reality, such guesses may be very different from true values of the parameters, leading to inefficient test plans. To address this problem, we propose a sequential Bayesian strategy for planning of ALTs and a Bayesian estimation procedure for updating the parameter estimates sequentially. The proposed approach is motivated by ALT for polymer composite materials, but are generally applicable to a wide range of testing scenarios. Through the proposed sequential Bayesian design, one can efficiently collect data and then make predictions for the field performance. We use extensive simulations to evaluate the properties of the proposed sequential test planning strategy. We compare the proposed method to various traditional non-sequential optimum designs. Our results show that the proposed strategy is more robust and efficient, as compared to existing non-sequential optimum designs. Supplementary materials for this article are available online.
Year
DOI
Venue
2018
10.1080/00401706.2018.1437475
TECHNOMETRICS
Keywords
Field
DocType
Accelerated life test,Bayesian test planning,Fatigue testing,MCMC,Optimum design,Sequential design
Data quality,Test plan,Markov chain Monte Carlo,Polymer composites,Fatigue testing,Artificial intelligence,Statistics,Sequential analysis,Bayes estimator,Machine learning,Mathematics,Bayesian probability
Journal
Volume
Issue
ISSN
60.0
4.0
0040-1706
Citations 
PageRank 
References 
0
0.34
4
Authors
4
Name
Order
Citations
PageRank
I-Chen Lee100.34
Yili Hong229028.48
Sheng-Tsaing Tseng324621.68
Tirthankar Dasgupta47626.41